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Tomato Detection Dataset

89511
Tagagriculture
Taskobject detection
Release YearMade in 2020
LicenseCC0 1.0
Download180 MB

Summary #

Dataset LinkHomepage

Tomato Detection is a dataset for an object detection task. Possible applications of the dataset could be in the agricultural industry.

The dataset consists of 895 images with 4930 labeled objects belonging to 1 single class (tomato).

Images in the Tomato Detection dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2020.

Dataset Poster

Explore #

Tomato Detection dataset has 895 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
OpenSample annotation mask from Tomato DetectionSample image from Tomato Detection
πŸ‘€
Have a look at 895 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-1 of 1
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
tomatoβž”
rectangle
895
4930
5.51
12.38%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-1 of 1
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
tomato
rectangle
4930
2.44%
87.88%
0.04%
5px
1.2%
487px
97.4%
55px
12.18%
6px
1.2%
409px
96.5%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 4930 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 4930
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
tomato
rectangle
tomato517.png
500 x 400
47px
9.4%
39px
9.75%
0.92%
2βž”
tomato
rectangle
tomato517.png
500 x 400
43px
8.6%
51px
12.75%
1.1%
3βž”
tomato
rectangle
tomato517.png
500 x 400
35px
7%
31px
7.75%
0.54%
4βž”
tomato
rectangle
tomato517.png
500 x 400
32px
6.4%
30px
7.5%
0.48%
5βž”
tomato
rectangle
tomato517.png
500 x 400
38px
7.6%
34px
8.5%
0.65%
6βž”
tomato
rectangle
tomato517.png
500 x 400
29px
5.8%
35px
8.75%
0.51%
7βž”
tomato
rectangle
tomato890.png
500 x 400
39px
7.8%
37px
9.25%
0.72%
8βž”
tomato
rectangle
tomato890.png
500 x 400
37px
7.4%
46px
11.5%
0.85%
9βž”
tomato
rectangle
tomato107.png
500 x 400
53px
10.6%
38px
9.5%
1.01%
10βž”
tomato
rectangle
tomato107.png
500 x 400
58px
11.6%
62px
15.5%
1.8%

License #

Tomato Detection is under CC0 1.0 license.

Source

Citation #

If you make use of the Tomato Detection data, please cite the following reference:

@misc{make ml,
title={Tomato Dataset},
url={https://makeml.app/datasets/tomato},
journal={Make ML}}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-tomato-detection-dataset,
  title = { Visualization Tools for Tomato Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/tomato-detection } },
  url = { https://datasetninja.com/tomato-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset Tomato Detection can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Tomato Detection', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

. . .

Disclaimer #

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